語系:
繁體中文
English
說明(常見問題)
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Reciprocal recommender systems
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Reciprocal recommender systems/ by James Neve.
作者:
Neve, James.
出版者:
Cham :Springer Nature Switzerland : : 2025.,
面頁冊數:
xi, 107 p. :ill., digital ; : 24 cm.;
Contained By:
Springer Nature eBook
標題:
Machine Learning. -
電子資源:
https://doi.org/10.1007/978-3-031-85103-2
ISBN:
9783031851032
Reciprocal recommender systems
Neve, James.
Reciprocal recommender systems
[electronic resource] /by James Neve. - Cham :Springer Nature Switzerland :2025. - xi, 107 p. :ill., digital ;24 cm. - SpringerBriefs in computer science,2191-5776. - SpringerBriefs in computer science..
Preface -- 1. Introduction -- 2. Theoretical Background -- 3. Collaborative Filtering -- 4. Content-Based Filtering -- 5. Hybrid Filtering and Additional Approaches -- 6. Matching Theory -- 7. Ethical Concerns and Future Work.
This book provides an introduction to reciprocal recommendation. It starts with theory, and then moves on to concrete examples of the most successful algorithms in the field. Researchers and developers with a little background in machine learning will find many of the algorithms are straightforward to implement, and code samples are included to help with this. In addition to accessible algorithms, the book also examines some more cutting-edge research such as the recent interest in applying matching theory to reciprocal recommendation. These parts will be of interest both to developers who are looking to optimize their systems, and to researchers who might find avenues to further advance the field and develop new methods of recommending people to people. By the end of this book, the reader will have a comprehensive understanding of the state of the art in reciprocal recommendation and will be equipped to design and implement their own systems.
ISBN: 9783031851032
Standard No.: 10.1007/978-3-031-85103-2doiSubjects--Topical Terms:
1137723
Machine Learning.
LC Class. No.: ZA3084
Dewey Class. No.: 005.56
Reciprocal recommender systems
LDR
:02232nam a2200361 a 4500
001
1160662
003
DE-He213
005
20250228120735.0
006
m d
007
cr nn 008maaau
008
251029s2025 sz s 0 eng d
020
$a
9783031851032
$q
(electronic bk.)
020
$a
9783031851025
$q
(paper)
024
7
$a
10.1007/978-3-031-85103-2
$2
doi
035
$a
978-3-031-85103-2
040
$a
GP
$c
GP
041
0
$a
eng
050
4
$a
ZA3084
072
7
$a
UNH
$2
bicssc
072
7
$a
UND
$2
bicssc
072
7
$a
COM030000
$2
bisacsh
072
7
$a
UNH
$2
thema
072
7
$a
UND
$2
thema
082
0 4
$a
005.56
$2
23
090
$a
ZA3084
$b
.N511 2025
100
1
$a
Neve, James.
$3
1487720
245
1 0
$a
Reciprocal recommender systems
$h
[electronic resource] /
$c
by James Neve.
260
$a
Cham :
$c
2025.
$b
Springer Nature Switzerland :
$b
Imprint: Springer,
300
$a
xi, 107 p. :
$b
ill., digital ;
$c
24 cm.
490
1
$a
SpringerBriefs in computer science,
$x
2191-5776
505
0
$a
Preface -- 1. Introduction -- 2. Theoretical Background -- 3. Collaborative Filtering -- 4. Content-Based Filtering -- 5. Hybrid Filtering and Additional Approaches -- 6. Matching Theory -- 7. Ethical Concerns and Future Work.
520
$a
This book provides an introduction to reciprocal recommendation. It starts with theory, and then moves on to concrete examples of the most successful algorithms in the field. Researchers and developers with a little background in machine learning will find many of the algorithms are straightforward to implement, and code samples are included to help with this. In addition to accessible algorithms, the book also examines some more cutting-edge research such as the recent interest in applying matching theory to reciprocal recommendation. These parts will be of interest both to developers who are looking to optimize their systems, and to researchers who might find avenues to further advance the field and develop new methods of recommending people to people. By the end of this book, the reader will have a comprehensive understanding of the state of the art in reciprocal recommendation and will be equipped to design and implement their own systems.
650
2 4
$a
Machine Learning.
$3
1137723
650
1 4
$a
Information Storage and Retrieval.
$3
593926
650
0
$a
Recommender systems (Information filtering)
$3
713827
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer Nature eBook
830
0
$a
SpringerBriefs in computer science.
$3
883114
856
4 0
$u
https://doi.org/10.1007/978-3-031-85103-2
950
$a
Computer Science (SpringerNature-11645)
筆 0 讀者評論
多媒體
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼[密碼必須為2種組合(英文和數字)及長度為10碼以上]
登入